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LAMDA-HAD, an Extension to the LAMDA Classifier in the Context of Supervised Learning

Author

Listed:
  • Luis Morales

    (Departamento de Automatización y Control Industrial, Escuela Politécnica Nacional, Ladrón de Guevara E11-253, Quito, Ecuador)

  • José Aguilar

    (#x2020;CEMISID, Facultad de Ingeniería, Universidad de Los Andes, Mérida 5101, Venezuela‡GIDITIC, Universidad EAFIT, Medellín, Colombia)

  • Danilo Chávez

    (Departamento de Automatización y Control Industrial, Escuela Politécnica Nacional, Ladrón de Guevara E11-253, Quito, Ecuador)

  • Claudia Isaza

    (#xA7;SISTEMIC, Facultad de Ingeniería, Universidad de Antioquia, Calle 70, No. 52-21, Medellín, Colombia)

Abstract

This paper proposes a new approach to improve the performance of Learning Algorithm for Multivariable Data Analysis (LAMDA). This algorithm can be used for supervised and unsupervised learning, based on the calculation of the Global Adequacy Degree (GAD) of one individual to a class, through the contributions of all its descriptors. LAMDA has the capability of creating new classes after the training stage. If an individual does not have enough similarity to the preexisting classes, it is evaluated with respect to a threshold called the Non-Informative Class (NIC), this being the novelty of the algorithm. However, LAMDA has problems making good classifications, either because the NIC is constant for all classes, or because the GAD calculation is unreliable. In this work, its efficiency is improved by two strategies, the first one, by the calculation of adaptable NICs for each class, which prevents that correctly classified individuals create new classes; and the second one, by computing the Higher Adequacy Degree (HAD), which grants more robustness to the algorithm. LAMDA-HAD is validated by applying it in different benchmarks and comparing it with LAMDA and other classifiers, through a statistical analysis to determinate the cases in which our algorithm presents a better performance.

Suggested Citation

  • Luis Morales & José Aguilar & Danilo Chávez & Claudia Isaza, 2020. "LAMDA-HAD, an Extension to the LAMDA Classifier in the Context of Supervised Learning," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 283-316, January.
  • Handle: RePEc:wsi:ijitdm:v:19:y:2020:i:01:n:s0219622019500457
    DOI: 10.1142/S0219622019500457
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    References listed on IDEAS

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    1. Gang Kou & Yanqun Lu & Yi Peng & Yong Shi, 2012. "Evaluation Of Classification Algorithms Using Mcdm And Rank Correlation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 197-225.
    2. Zhang, Huanhuan & Kou, Gang & Peng, Yi, 2019. "Soft consensus cost models for group decision making and economic interpretations," European Journal of Operational Research, Elsevier, vol. 277(3), pages 964-980.
    3. Xin Ying Qiu & Padmini Srinivasan & Yong Hu, 2014. "Supervised learning models to predict firm performance with annual reports: An empirical study," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(2), pages 400-413, February.
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